class_docstr: A sampled, rank-based evaluator that applies a custom OGB evaluation.
class_docstr_tok: ["A","sampled",",","rank-based","evaluator","that","applies","a","custom","OGB","evaluation","."]
code: def __init__(self, filtered: bool = False, **kwargs):
      if filtered:
          raise ValueError(
              "OGB evaluator is already filtered, but not dynamically like other evaluators because "
              "it requires pre-calculated filtered negative triples. Therefore, it is not allowed to "
              "accept filtered=True"
          )
      super().__init__(**kwargs, filtered=filtered)
  
file_path: pykeen/src/pykeen/evaluation/ogb_evaluator.py
imports: from __future__ import annotations
  import logging
  from typing import Any, Collection, Dict, Iterable, List, Mapping, Optional, Tuple, Union
  import torch
  from torch_max_mem import maximize_memory_utilization
  from tqdm.auto import tqdm
  from .evaluator import MetricResults
  from .rank_based_evaluator import RankBasedMetricKey, RankBasedMetricResults, SampledRankBasedEvaluator
  from ..metrics import RankBasedMetric
  from ..metrics.ranking import HitsAtK, InverseHarmonicMeanRank
  from ..models import Model
  from ..typing import LABEL_HEAD, LABEL_TAIL, RANK_REALISTIC, SIDE_BOTH, ExtendedTarget, MappedTriples, Target
label_desc: Use image node_id 1 to create a new OGBEvaluator object from inherited base classes: SampledRankBasedEvaluator with example: obj = OGBEvaluator(filtered)